Volunteer Summary

CONSORT Flow Diagram

Overall status

Characteristic

Overall1

Control1

Treatment1

time_point

1st

120

58

62

2nd

86

52

34

1n

Demographic information

Characteristic

N

Overall, N = 1201

control, N = 581

treatment, N = 621

p-value2

age

120

38.15 ± 17.06 (18 - 148)

39.90 ± 19.46 (18 - 148)

36.51 ± 14.44 (20 - 70)

0.279

gender

120

0.298

female

86 (72%)

39 (67%)

47 (76%)

male

34 (28%)

19 (33%)

15 (24%)

occupation

120

0.659

civil

6 (5.0%)

2 (3.4%)

4 (6.5%)

clerk

23 (19%)

9 (16%)

14 (23%)

homemaker

8 (6.7%)

3 (5.2%)

5 (8.1%)

manager

16 (13%)

9 (16%)

7 (11%)

other

11 (9.2%)

4 (6.9%)

7 (11%)

professional

15 (12%)

11 (19%)

4 (6.5%)

retired

4 (3.3%)

2 (3.4%)

2 (3.2%)

service

5 (4.2%)

2 (3.4%)

3 (4.8%)

student

30 (25%)

15 (26%)

15 (24%)

unemploy

2 (1.7%)

1 (1.7%)

1 (1.6%)

working_status

120

76 (63%)

37 (64%)

39 (63%)

0.919

marital

120

0.477

divorced

4 (3.3%)

1 (1.7%)

3 (4.8%)

married

27 (22%)

15 (26%)

12 (19%)

single

88 (73%)

41 (71%)

47 (76%)

widowed

1 (0.8%)

1 (1.7%)

0 (0%)

marital_r

120

0.689

married

27 (22%)

15 (26%)

12 (19%)

other

5 (4.2%)

2 (3.4%)

3 (4.8%)

single

88 (73%)

41 (71%)

47 (76%)

education

120

0.074

primary

0 (0%)

0 (0%)

0 (0%)

secondary

14 (12%)

3 (5.2%)

11 (18%)

post-secondary

20 (17%)

12 (21%)

8 (13%)

university

86 (72%)

43 (74%)

43 (69%)

university_edu

120

86 (72%)

43 (74%)

43 (69%)

0.561

family_income

120

0.541

0_10000

13 (11%)

5 (8.6%)

8 (13%)

10001_20000

22 (18%)

8 (14%)

14 (23%)

20001_30000

23 (19%)

11 (19%)

12 (19%)

30001_40000

20 (17%)

10 (17%)

10 (16%)

40000_above

42 (35%)

24 (41%)

18 (29%)

high_income

120

62 (52%)

34 (59%)

28 (45%)

0.140

religion

120

0.649

buddhism

5 (4.2%)

4 (6.9%)

1 (1.6%)

catholic

5 (4.2%)

2 (3.4%)

3 (4.8%)

christianity

47 (39%)

23 (40%)

24 (39%)

nil

61 (51%)

29 (50%)

32 (52%)

other

1 (0.8%)

0 (0%)

1 (1.6%)

taoism

1 (0.8%)

0 (0%)

1 (1.6%)

religion_r

120

0.915

christianity

52 (43%)

25 (43%)

27 (44%)

nil

61 (51%)

29 (50%)

32 (52%)

other

7 (5.8%)

4 (6.9%)

3 (4.8%)

source

120

0.067

bokss

51 (42%)

20 (34%)

31 (50%)

facebook

17 (14%)

13 (22%)

4 (6.5%)

instagram

9 (7.5%)

6 (10%)

3 (4.8%)

other

19 (16%)

9 (16%)

10 (16%)

refresh

24 (20%)

10 (17%)

14 (23%)

1Mean ± SD (Range); n (%)

2Two Sample t-test; Pearson's Chi-squared test; Fisher's exact test

Measurement

Characteristic

N

Overall, N = 1201

control, N = 581

treatment, N = 621

p-value2

sets

120

19.20 ± 2.18 (15 - 25)

19.02 ± 2.03 (15 - 24)

19.37 ± 2.31 (15 - 25)

0.377

setv

120

11.14 ± 1.64 (7 - 15)

11.03 ± 1.56 (8 - 15)

11.24 ± 1.71 (7 - 15)

0.490

maks

120

44.92 ± 3.63 (36 - 57)

44.67 ± 3.59 (36 - 52)

45.16 ± 3.68 (38 - 57)

0.463

ibs

120

15.44 ± 2.45 (5 - 20)

15.41 ± 2.14 (10 - 20)

15.47 ± 2.72 (5 - 20)

0.904

ers_e

120

12.22 ± 1.46 (8 - 15)

12.14 ± 1.47 (8 - 15)

12.29 ± 1.45 (9 - 15)

0.569

ers_r

120

11.11 ± 1.58 (7 - 15)

11.02 ± 1.57 (7 - 14)

11.19 ± 1.59 (8 - 15)

0.543

pss_pa

120

44.62 ± 4.47 (30 - 54)

44.47 ± 4.26 (30 - 54)

44.76 ± 4.68 (31 - 54)

0.722

pss_ps

120

26.64 ± 8.34 (12 - 56)

26.67 ± 7.63 (13 - 42)

26.61 ± 9.02 (12 - 56)

0.969

pss

120

45.02 ± 11.85 (21 - 77)

45.21 ± 11.26 (22 - 72)

44.85 ± 12.47 (21 - 77)

0.872

rki_responsible

120

21.01 ± 4.13 (7 - 32)

20.95 ± 4.11 (13 - 29)

21.06 ± 4.18 (7 - 32)

0.878

rki_nonlinear

120

13.30 ± 2.75 (6 - 22)

13.12 ± 2.54 (6 - 20)

13.47 ± 2.94 (7 - 22)

0.492

rki_peer

120

20.58 ± 2.15 (16 - 25)

20.47 ± 2.07 (16 - 25)

20.68 ± 2.23 (16 - 25)

0.591

rki_expect

120

4.75 ± 1.09 (2 - 8)

4.60 ± 1.11 (2 - 8)

4.89 ± 1.07 (2 - 7)

0.157

rki

120

59.63 ± 6.10 (44 - 81)

59.14 ± 5.86 (45 - 76)

60.10 ± 6.33 (44 - 81)

0.392

raq_possible

120

15.66 ± 1.79 (12 - 20)

15.74 ± 1.89 (12 - 20)

15.58 ± 1.71 (12 - 20)

0.626

raq_difficulty

120

12.42 ± 1.39 (9 - 15)

12.53 ± 1.38 (9 - 15)

12.31 ± 1.41 (9 - 15)

0.373

raq

120

28.08 ± 2.90 (21 - 35)

28.28 ± 2.97 (21 - 35)

27.89 ± 2.85 (21 - 35)

0.466

who

120

14.63 ± 4.46 (3 - 25)

14.62 ± 4.24 (6 - 25)

14.65 ± 4.68 (3 - 25)

0.976

phq

120

3.76 ± 3.81 (0 - 18)

3.66 ± 3.73 (0 - 17)

3.85 ± 3.91 (0 - 18)

0.776

gad

120

3.23 ± 3.57 (0 - 21)

3.38 ± 4.11 (0 - 21)

3.08 ± 3.00 (0 - 12)

0.649

nb_pcs

120

51.64 ± 7.15 (25 - 63)

51.88 ± 7.17 (25 - 63)

51.42 ± 7.18 (27 - 62)

0.729

nb_mcs

120

50.24 ± 8.59 (22 - 70)

50.20 ± 8.89 (22 - 68)

50.28 ± 8.37 (35 - 70)

0.960

1Mean ± SD (Range)

2Two Sample t-test

Data analysis

Table

Group

Characteristic

Beta

SE1

95% CI1

p-value

sets

(Intercept)

19.0

0.274

18.5, 19.6

group

control

—

—

—

treatment

0.354

0.381

-0.393, 1.10

0.355

time_point

1st

—

—

—

2nd

-0.153

0.305

-0.750, 0.445

0.618

group * time_point

treatment * 2nd

0.319

0.470

-0.602, 1.24

0.498

Pseudo R square

0.015

setv

(Intercept)

11.0

0.216

10.6, 11.5

group

control

—

—

—

treatment

0.207

0.300

-0.381, 0.796

0.491

time_point

1st

—

—

—

2nd

0.324

0.215

-0.098, 0.745

0.136

group * time_point

treatment * 2nd

-0.155

0.333

-0.808, 0.498

0.642

Pseudo R square

0.007

maks

(Intercept)

44.7

0.485

43.7, 45.6

group

control

—

—

—

treatment

0.489

0.674

-0.833, 1.81

0.470

time_point

1st

—

—

—

2nd

-0.209

0.400

-0.992, 0.575

0.603

group * time_point

treatment * 2nd

0.276

0.625

-0.949, 1.50

0.660

Pseudo R square

0.007

ibs

(Intercept)

15.4

0.310

14.8, 16.0

group

control

—

—

—

treatment

0.054

0.432

-0.792, 0.900

0.901

time_point

1st

—

—

—

2nd

0.167

0.244

-0.311, 0.646

0.495

group * time_point

treatment * 2nd

0.438

0.382

-0.311, 1.19

0.255

Pseudo R square

0.009

ers_e

(Intercept)

12.1

0.187

11.8, 12.5

group

control

—

—

—

treatment

0.152

0.260

-0.358, 0.663

0.559

time_point

1st

—

—

—

2nd

-0.290

0.174

-0.632, 0.052

0.100

group * time_point

treatment * 2nd

0.415

0.271

-0.117, 0.947

0.130

Pseudo R square

0.020

ers_r

(Intercept)

11.0

0.193

10.6, 11.4

group

control

—

—

—

treatment

0.176

0.269

-0.350, 0.703

0.513

time_point

1st

—

—

—

2nd

0.097

0.231

-0.356, 0.551

0.675

group * time_point

treatment * 2nd

0.195

0.354

-0.500, 0.890

0.583

Pseudo R square

0.011

pss_pa

(Intercept)

44.5

0.581

43.3, 45.6

group

control

—

—

—

treatment

0.293

0.809

-1.29, 1.88

0.718

time_point

1st

—

—

—

2nd

-0.991

0.611

-2.19, 0.206

0.108

group * time_point

treatment * 2nd

0.501

0.944

-1.35, 2.35

0.597

Pseudo R square

0.012

pss_ps

(Intercept)

26.7

1.060

24.6, 28.7

group

control

—

—

—

treatment

-0.060

1.474

-2.95, 2.83

0.968

time_point

1st

—

—

—

2nd

0.951

0.920

-0.851, 2.75

0.304

group * time_point

treatment * 2nd

-1.65

1.435

-4.47, 1.16

0.253

Pseudo R square

0.005

pss

(Intercept)

45.2

1.506

42.3, 48.2

group

control

—

—

—

treatment

-0.352

2.095

-4.46, 3.75

0.867

time_point

1st

—

—

—

2nd

1.98

1.305

-0.580, 4.53

0.134

group * time_point

treatment * 2nd

-2.05

2.036

-6.04, 1.94

0.317

Pseudo R square

0.007

rki_responsible

(Intercept)

20.9

0.537

19.9, 22.0

group

control

—

—

—

treatment

0.116

0.746

-1.35, 1.58

0.876

time_point

1st

—

—

—

2nd

0.073

0.499

-0.905, 1.05

0.883

group * time_point

treatment * 2nd

-0.036

0.776

-1.56, 1.49

0.963

Pseudo R square

0.000

rki_nonlinear

(Intercept)

13.1

0.373

12.4, 13.9

group

control

—

—

—

treatment

0.347

0.518

-0.669, 1.36

0.504

time_point

1st

—

—

—

2nd

-0.270

0.355

-0.965, 0.425

0.448

group * time_point

treatment * 2nd

0.546

0.551

-0.534, 1.63

0.325

Pseudo R square

0.012

rki_peer

(Intercept)

20.5

0.285

19.9, 21.0

group

control

—

—

—

treatment

0.212

0.396

-0.565, 0.989

0.594

time_point

1st

—

—

—

2nd

0.005

0.280

-0.544, 0.553

0.987

group * time_point

treatment * 2nd

0.142

0.434

-0.709, 0.994

0.744

Pseudo R square

0.004

rki_expect

(Intercept)

4.60

0.136

4.34, 4.87

group

control

—

—

—

treatment

0.284

0.189

-0.087, 0.654

0.135

time_point

1st

—

—

—

2nd

0.138

0.154

-0.165, 0.441

0.374

group * time_point

treatment * 2nd

0.089

0.238

-0.377, 0.554

0.710

Pseudo R square

0.028

rki

(Intercept)

59.1

0.793

57.6, 60.7

group

control

—

—

—

treatment

0.959

1.103

-1.20, 3.12

0.386

time_point

1st

—

—

—

2nd

-0.051

0.743

-1.51, 1.40

0.946

group * time_point

treatment * 2nd

0.765

1.155

-1.50, 3.03

0.510

Pseudo R square

0.012

raq_possible

(Intercept)

15.7

0.230

15.3, 16.2

group

control

—

—

—

treatment

-0.161

0.320

-0.788, 0.466

0.616

time_point

1st

—

—

—

2nd

-0.378

0.248

-0.864, 0.108

0.131

group * time_point

treatment * 2nd

0.837

0.383

0.086, 1.59

0.031

Pseudo R square

0.016

raq_difficulty

(Intercept)

12.5

0.178

12.2, 12.9

group

control

—

—

—

treatment

-0.228

0.247

-0.712, 0.256

0.357

time_point

1st

—

—

—

2nd

-0.128

0.173

-0.466, 0.210

0.461

group * time_point

treatment * 2nd

0.307

0.268

-0.218, 0.832

0.255

Pseudo R square

0.005

raq

(Intercept)

28.3

0.373

27.5, 29.0

group

control

—

—

—

treatment

-0.389

0.519

-1.41, 0.628

0.455

time_point

1st

—

—

—

2nd

-0.481

0.356

-1.18, 0.218

0.181

group * time_point

treatment * 2nd

1.12

0.554

0.038, 2.21

0.045

Pseudo R square

0.009

who

(Intercept)

14.6

0.586

13.5, 15.8

group

control

—

—

—

treatment

0.024

0.815

-1.57, 1.62

0.976

time_point

1st

—

—

—

2nd

-0.187

0.470

-1.11, 0.735

0.692

group * time_point

treatment * 2nd

0.838

0.736

-0.605, 2.28

0.258

Pseudo R square

0.004

phq

(Intercept)

3.66

0.491

2.69, 4.62

group

control

—

—

—

treatment

0.200

0.683

-1.14, 1.54

0.770

time_point

1st

—

—

—

2nd

0.118

0.324

-0.516, 0.753

0.716

group * time_point

treatment * 2nd

-0.140

0.509

-1.14, 0.858

0.784

Pseudo R square

0.000

gad

(Intercept)

3.38

0.456

2.49, 4.27

group

control

—

—

—

treatment

-0.299

0.634

-1.54, 0.944

0.638

time_point

1st

—

—

—

2nd

-0.084

0.361

-0.791, 0.623

0.817

group * time_point

treatment * 2nd

0.090

0.565

-1.02, 1.20

0.873

Pseudo R square

0.001

nb_pcs

(Intercept)

51.9

0.909

50.1, 53.7

group

control

—

—

—

treatment

-0.455

1.264

-2.93, 2.02

0.719

time_point

1st

—

—

—

2nd

-0.830

0.742

-2.28, 0.624

0.266

group * time_point

treatment * 2nd

1.52

1.161

-0.759, 3.79

0.195

Pseudo R square

0.003

nb_mcs

(Intercept)

50.2

1.106

48.0, 52.4

group

control

—

—

—

treatment

0.080

1.539

-2.94, 3.10

0.959

time_point

1st

—

—

—

2nd

0.994

1.023

-1.01, 3.00

0.333

group * time_point

treatment * 2nd

-0.510

1.592

-3.63, 2.61

0.749

Pseudo R square

0.002

1SE = Standard Error, CI = Confidence Interval

Text

sets

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict sets with group and time_point (formula: sets ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.44) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 19.02 (95% CI [18.48, 19.55], t(200) = 69.40, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.35, 95% CI [-0.39, 1.10], t(200) = 0.93, p = 0.353; Std. beta = 0.17, 95% CI [-0.19, 0.53])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.15, 95% CI [-0.75, 0.45], t(200) = -0.50, p = 0.617; Std. beta = -0.07, 95% CI [-0.36, 0.21])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.32, 95% CI [-0.60, 1.24], t(200) = 0.68, p = 0.497; Std. beta = 0.15, 95% CI [-0.29, 0.59])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

setv

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict setv with group and time_point (formula: setv ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.55) and the part related to the fixed effects alone (marginal R2) is of 7.44e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 11.03 (95% CI [10.61, 11.46], t(200) = 51.10, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.21, 95% CI [-0.38, 0.80], t(200) = 0.69, p = 0.490; Std. beta = 0.13, 95% CI [-0.23, 0.48])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.32, 95% CI [-0.10, 0.74], t(200) = 1.51, p = 0.132; Std. beta = 0.20, 95% CI [-0.06, 0.45])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.16, 95% CI [-0.81, 0.50], t(200) = -0.47, p = 0.641; Std. beta = -0.09, 95% CI [-0.49, 0.30])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

maks

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict maks with group and time_point (formula: maks ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.69) and the part related to the fixed effects alone (marginal R2) is of 7.27e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 44.67 (95% CI [43.72, 45.62], t(200) = 92.15, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.49, 95% CI [-0.83, 1.81], t(200) = 0.72, p = 0.469; Std. beta = 0.13, 95% CI [-0.23, 0.49])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.21, 95% CI [-0.99, 0.58], t(200) = -0.52, p = 0.602; Std. beta = -0.06, 95% CI [-0.27, 0.16])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.28, 95% CI [-0.95, 1.50], t(200) = 0.44, p = 0.659; Std. beta = 0.07, 95% CI [-0.26, 0.41])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ibs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ibs with group and time_point (formula: ibs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.72) and the part related to the fixed effects alone (marginal R2) is of 9.22e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 15.41 (95% CI [14.81, 16.02], t(200) = 49.66, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.05, 95% CI [-0.79, 0.90], t(200) = 0.12, p = 0.901; Std. beta = 0.02, 95% CI [-0.34, 0.39])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.17, 95% CI [-0.31, 0.65], t(200) = 0.69, p = 0.493; Std. beta = 0.07, 95% CI [-0.13, 0.28])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.44, 95% CI [-0.31, 1.19], t(200) = 1.15, p = 0.252; Std. beta = 0.19, 95% CI [-0.13, 0.51])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ers_e

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ers_e with group and time_point (formula: ers_e ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.61) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 12.14 (95% CI [11.77, 12.50], t(200) = 64.88, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.15, 95% CI [-0.36, 0.66], t(200) = 0.59, p = 0.558; Std. beta = 0.11, 95% CI [-0.25, 0.46])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.29, 95% CI [-0.63, 0.05], t(200) = -1.66, p = 0.097; Std. beta = -0.20, 95% CI [-0.44, 0.04])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.41, 95% CI [-0.12, 0.95], t(200) = 1.53, p = 0.126; Std. beta = 0.29, 95% CI [-0.08, 0.66])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ers_r

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ers_r with group and time_point (formula: ers_r ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.34) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 11.02 (95% CI [10.64, 11.40], t(200) = 57.03, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.18, 95% CI [-0.35, 0.70], t(200) = 0.66, p = 0.512; Std. beta = 0.12, 95% CI [-0.24, 0.48])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.10, 95% CI [-0.36, 0.55], t(200) = 0.42, p = 0.674; Std. beta = 0.07, 95% CI [-0.24, 0.37])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.19, 95% CI [-0.50, 0.89], t(200) = 0.55, p = 0.582; Std. beta = 0.13, 95% CI [-0.34, 0.61])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss_pa

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss_pa with group and time_point (formula: pss_pa ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.50) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 44.47 (95% CI [43.33, 45.60], t(200) = 76.51, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.29, 95% CI [-1.29, 1.88], t(200) = 0.36, p = 0.717; Std. beta = 0.07, 95% CI [-0.29, 0.42])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.99, 95% CI [-2.19, 0.21], t(200) = -1.62, p = 0.105; Std. beta = -0.22, 95% CI [-0.49, 0.05])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.50, 95% CI [-1.35, 2.35], t(200) = 0.53, p = 0.596; Std. beta = 0.11, 95% CI [-0.31, 0.53])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss_ps

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss_ps with group and time_point (formula: pss_ps ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.66) and the part related to the fixed effects alone (marginal R2) is of 4.85e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 26.67 (95% CI [24.60, 28.75], t(200) = 25.17, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.06, 95% CI [-2.95, 2.83], t(200) = -0.04, p = 0.968; Std. beta = -7.55e-03, 95% CI [-0.37, 0.36])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.95, 95% CI [-0.85, 2.75], t(200) = 1.03, p = 0.301; Std. beta = 0.12, 95% CI [-0.11, 0.35])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -1.65, 95% CI [-4.47, 1.16], t(200) = -1.15, p = 0.250; Std. beta = -0.21, 95% CI [-0.57, 0.15])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss with group and time_point (formula: pss ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.66) and the part related to the fixed effects alone (marginal R2) is of 7.20e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 45.21 (95% CI [42.26, 48.16], t(200) = 30.03, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.35, 95% CI [-4.46, 3.75], t(200) = -0.17, p = 0.867; Std. beta = -0.03, 95% CI [-0.39, 0.33])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 1.98, 95% CI [-0.58, 4.53], t(200) = 1.52, p = 0.130; Std. beta = 0.17, 95% CI [-0.05, 0.40])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -2.05, 95% CI [-6.04, 1.94], t(200) = -1.01, p = 0.315; Std. beta = -0.18, 95% CI [-0.53, 0.17])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_responsible

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_responsible with group and time_point (formula: rki_responsible ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 1.86e-04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 20.95 (95% CI [19.90, 22.00], t(200) = 39.05, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.12, 95% CI [-1.35, 1.58], t(200) = 0.16, p = 0.876; Std. beta = 0.03, 95% CI [-0.34, 0.39])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.07, 95% CI [-0.90, 1.05], t(200) = 0.15, p = 0.883; Std. beta = 0.02, 95% CI [-0.23, 0.26])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.04, 95% CI [-1.56, 1.49], t(200) = -0.05, p = 0.963; Std. beta = -9.08e-03, 95% CI [-0.39, 0.37])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_nonlinear

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_nonlinear with group and time_point (formula: rki_nonlinear ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 13.12 (95% CI [12.39, 13.85], t(200) = 35.21, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.35, 95% CI [-0.67, 1.36], t(200) = 0.67, p = 0.503; Std. beta = 0.12, 95% CI [-0.24, 0.49])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.27, 95% CI [-0.96, 0.42], t(200) = -0.76, p = 0.446; Std. beta = -0.10, 95% CI [-0.34, 0.15])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.55, 95% CI [-0.53, 1.63], t(200) = 0.99, p = 0.322; Std. beta = 0.19, 95% CI [-0.19, 0.58])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_peer

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_peer with group and time_point (formula: rki_peer ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 4.12e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 20.47 (95% CI [19.91, 21.02], t(200) = 71.86, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.21, 95% CI [-0.56, 0.99], t(200) = 0.53, p = 0.593; Std. beta = 0.10, 95% CI [-0.26, 0.46])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 4.56e-03, 95% CI [-0.54, 0.55], t(200) = 0.02, p = 0.987; Std. beta = 2.12e-03, 95% CI [-0.25, 0.26])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.14, 95% CI [-0.71, 0.99], t(200) = 0.33, p = 0.743; Std. beta = 0.07, 95% CI [-0.33, 0.46])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_expect

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_expect with group and time_point (formula: rki_expect ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.42) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 4.60 (95% CI [4.34, 4.87], t(200) = 33.90, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.28, 95% CI [-0.09, 0.65], t(200) = 1.50, p = 0.133; Std. beta = 0.27, 95% CI [-0.08, 0.63])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.14, 95% CI [-0.16, 0.44], t(200) = 0.89, p = 0.372; Std. beta = 0.13, 95% CI [-0.16, 0.42])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.09, 95% CI [-0.38, 0.55], t(200) = 0.37, p = 0.709; Std. beta = 0.09, 95% CI [-0.36, 0.53])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki with group and time_point (formula: rki ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 59.14 (95% CI [57.58, 60.69], t(200) = 74.57, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.96, 95% CI [-1.20, 3.12], t(200) = 0.87, p = 0.385; Std. beta = 0.16, 95% CI [-0.20, 0.53])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.05, 95% CI [-1.51, 1.40], t(200) = -0.07, p = 0.945; Std. beta = -8.57e-03, 95% CI [-0.25, 0.24])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.77, 95% CI [-1.50, 3.03], t(200) = 0.66, p = 0.508; Std. beta = 0.13, 95% CI [-0.25, 0.51])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq_possible

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq_possible with group and time_point (formula: raq_possible ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.47) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 15.74 (95% CI [15.29, 16.19], t(200) = 68.44, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.16, 95% CI [-0.79, 0.47], t(200) = -0.50, p = 0.615; Std. beta = -0.09, 95% CI [-0.45, 0.27])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.38, 95% CI [-0.86, 0.11], t(200) = -1.52, p = 0.128; Std. beta = -0.21, 95% CI [-0.49, 0.06])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 0.84, 95% CI [0.09, 1.59], t(200) = 2.19, p = 0.029; Std. beta = 0.48, 95% CI [0.05, 0.90])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq_difficulty

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq_difficulty with group and time_point (formula: raq_difficulty ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.57) and the part related to the fixed effects alone (marginal R2) is of 4.54e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 12.53 (95% CI [12.19, 12.88], t(200) = 70.61, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.23, 95% CI [-0.71, 0.26], t(200) = -0.92, p = 0.356; Std. beta = -0.17, 95% CI [-0.53, 0.19])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.13, 95% CI [-0.47, 0.21], t(200) = -0.74, p = 0.459; Std. beta = -0.09, 95% CI [-0.35, 0.16])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.31, 95% CI [-0.22, 0.83], t(200) = 1.14, p = 0.252; Std. beta = 0.23, 95% CI [-0.16, 0.62])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq with group and time_point (formula: raq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 9.35e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 28.28 (95% CI [27.54, 29.01], t(200) = 75.80, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.39, 95% CI [-1.41, 0.63], t(200) = -0.75, p = 0.454; Std. beta = -0.14, 95% CI [-0.49, 0.22])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.48, 95% CI [-1.18, 0.22], t(200) = -1.35, p = 0.177; Std. beta = -0.17, 95% CI [-0.41, 0.08])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 1.12, 95% CI [0.04, 2.21], t(200) = 2.03, p = 0.043; Std. beta = 0.40, 95% CI [0.01, 0.78])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

who

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict who with group and time_point (formula: who ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.71) and the part related to the fixed effects alone (marginal R2) is of 3.98e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 14.62 (95% CI [13.47, 15.77], t(200) = 24.97, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.02, 95% CI [-1.57, 1.62], t(200) = 0.03, p = 0.976; Std. beta = 5.55e-03, 95% CI [-0.36, 0.37])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.19, 95% CI [-1.11, 0.74], t(200) = -0.40, p = 0.691; Std. beta = -0.04, 95% CI [-0.25, 0.17])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.84, 95% CI [-0.61, 2.28], t(200) = 1.14, p = 0.255; Std. beta = 0.19, 95% CI [-0.14, 0.52])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

phq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict phq with group and time_point (formula: phq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.80) and the part related to the fixed effects alone (marginal R2) is of 4.69e-04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.66 (95% CI [2.69, 4.62], t(200) = 7.45, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.20, 95% CI [-1.14, 1.54], t(200) = 0.29, p = 0.770; Std. beta = 0.05, 95% CI [-0.31, 0.42])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.12, 95% CI [-0.52, 0.75], t(200) = 0.37, p = 0.715; Std. beta = 0.03, 95% CI [-0.14, 0.20])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.14, 95% CI [-1.14, 0.86], t(200) = -0.28, p = 0.783; Std. beta = -0.04, 95% CI [-0.31, 0.23])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

gad

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict gad with group and time_point (formula: gad ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.72) and the part related to the fixed effects alone (marginal R2) is of 1.44e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.38 (95% CI [2.49, 4.27], t(200) = 7.42, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.30, 95% CI [-1.54, 0.94], t(200) = -0.47, p = 0.638; Std. beta = -0.09, 95% CI [-0.44, 0.27])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.08, 95% CI [-0.79, 0.62], t(200) = -0.23, p = 0.816; Std. beta = -0.02, 95% CI [-0.23, 0.18])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.09, 95% CI [-1.02, 1.20], t(200) = 0.16, p = 0.873; Std. beta = 0.03, 95% CI [-0.29, 0.34])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_pcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_pcs with group and time_point (formula: nb_pcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.70) and the part related to the fixed effects alone (marginal R2) is of 3.14e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 51.88 (95% CI [50.10, 53.66], t(200) = 57.10, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.46, 95% CI [-2.93, 2.02], t(200) = -0.36, p = 0.719; Std. beta = -0.07, 95% CI [-0.42, 0.29])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.83, 95% CI [-2.28, 0.62], t(200) = -1.12, p = 0.263; Std. beta = -0.12, 95% CI [-0.33, 0.09])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 1.52, 95% CI [-0.76, 3.79], t(200) = 1.31, p = 0.192; Std. beta = 0.22, 95% CI [-0.11, 0.54])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_mcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_mcs with group and time_point (formula: nb_mcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.61) and the part related to the fixed effects alone (marginal R2) is of 2.38e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 50.20 (95% CI [48.04, 52.37], t(200) = 45.38, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.08, 95% CI [-2.94, 3.10], t(200) = 0.05, p = 0.959; Std. beta = 9.51e-03, 95% CI [-0.35, 0.37])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.99, 95% CI [-1.01, 3.00], t(200) = 0.97, p = 0.331; Std. beta = 0.12, 95% CI [-0.12, 0.36])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.51, 95% CI [-3.63, 2.61], t(200) = -0.32, p = 0.749; Std. beta = -0.06, 95% CI [-0.43, 0.31])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

Likelihood ratio tests

outcome

model

npar

AIC

BIC

logLik

deviance

Chisq

Df

p

sets

null

3

874.767

884.751

-434.384

868.767

sets

random

6

878.282

898.249

-433.141

866.282

2.486

3

0.478

setv

null

3

764.006

773.990

-379.003

758.006

setv

random

6

767.121

787.089

-377.561

755.121

2.885

3

0.410

maks

null

3

1,070.308

1,080.291

-532.154

1,064.308

maks

random

6

1,075.090

1,095.057

-531.545

1,063.090

1.218

3

0.749

ibs

null

3

883.341

893.325

-438.671

877.341

ibs

random

6

884.490

904.457

-436.245

872.490

4.852

3

0.183

ers_e

null

3

698.649

708.633

-346.325

692.649

ers_e

random

6

699.682

719.649

-343.841

687.682

4.967

3

0.174

ers_r

null

3

737.815

747.799

-365.907

731.815

ers_r

random

6

741.476

761.443

-364.738

729.476

2.339

3

0.505

pss_pa

null

3

1,179.390

1,189.373

-586.695

1,173.390

pss_pa

random

6

1,181.621

1,201.588

-584.810

1,169.621

3.769

3

0.288

pss_ps

null

3

1,400.165

1,410.148

-697.082

1,394.165

pss_ps

random

6

1,404.403

1,424.370

-696.201

1,392.403

1.762

3

0.623

pss

null

3

1,545.676

1,555.659

-769.838

1,539.676

pss

random

6

1,548.929

1,568.897

-768.465

1,536.929

2.746

3

0.432

rki_responsible

null

3

1,127.455

1,137.439

-560.728

1,121.455

rki_responsible

random

6

1,133.411

1,153.378

-560.705

1,121.411

0.044

3

0.998

rki_nonlinear

null

3

982.640

992.624

-488.320

976.640

rki_nonlinear

random

6

986.241

1,006.208

-487.120

974.241

2.399

3

0.494

rki_peer

null

3

874.331

884.315

-434.166

868.331

rki_peer

random

6

879.625

899.592

-433.812

867.625

0.707

3

0.872

rki_expect

null

3

590.825

600.809

-292.413

584.825

rki_expect

random

6

591.301

611.268

-289.650

579.301

5.524

3

0.137

rki

null

3

1,291.403

1,301.387

-642.702

1,285.403

rki

random

6

1,295.327

1,315.294

-641.663

1,283.327

2.076

3

0.557

raq_possible

null

3

801.852

811.836

-397.926

795.852

raq_possible

random

6

802.725

822.692

-395.362

790.725

5.127

3

0.163

raq_difficulty

null

3

679.083

689.067

-336.542

673.083

raq_difficulty

random

6

683.490

703.458

-335.745

671.490

1.593

3

0.661

raq

null

3

985.346

995.330

-489.673

979.346

raq

random

6

987.213

1,007.180

-487.606

975.213

4.133

3

0.247

who

null

3

1,144.798

1,154.782

-569.399

1,138.798

who

random

6

1,149.140

1,169.108

-568.570

1,137.140

1.658

3

0.646

phq

null

3

1,040.877

1,050.860

-517.438

1,034.877

phq

random

6

1,046.691

1,066.659

-517.346

1,034.691

0.185

3

0.980

gad

null

3

1,037.968

1,047.952

-515.984

1,031.968

gad

random

6

1,043.723

1,063.690

-515.862

1,031.723

0.245

3

0.970

nb_pcs

null

3

1,328.335

1,338.319

-661.168

1,322.335

nb_pcs

random

6

1,332.466

1,352.433

-660.233

1,320.466

1.869

3

0.600

nb_mcs

null

3

1,425.882

1,435.866

-709.941

1,419.882

nb_mcs

random

6

1,430.736

1,450.704

-709.368

1,418.736

1.146

3

0.766

Post hoc analysis text

Table

outcome

time

control

treatment

between

n

estimate

within es

n

estimate

within es

p

es

sets

1st

58

19.02 ± 2.09

62

19.37 ± 2.09

0.355

-0.224

sets

2nd

52

18.86 ± 2.07

0.097

34

19.54 ± 2.01

-0.105

0.135

-0.426

setv

1st

58

11.03 ± 1.64

62

11.24 ± 1.64

0.491

-0.187

setv

2nd

52

11.36 ± 1.62

-0.292

34

11.41 ± 1.53

-0.152

0.881

-0.047

maks

1st

58

44.67 ± 3.69

62

45.16 ± 3.69

0.470

-0.238

maks

2nd

52

44.46 ± 3.60

0.102

34

45.23 ± 3.28

-0.033

0.310

-0.372

ibs

1st

58

15.41 ± 2.36

62

15.47 ± 2.36

0.901

-0.043

ibs

2nd

52

15.58 ± 2.30

-0.133

34

16.07 ± 2.07

-0.483

0.304

-0.393

ers_e

1st

58

12.14 ± 1.42

62

12.29 ± 1.42

0.559

-0.170

ers_e

2nd

52

11.85 ± 1.40

0.322

34

12.42 ± 1.31

-0.139

0.057

-0.631

ers_r

1st

58

11.02 ± 1.47

62

11.19 ± 1.47

0.513

-0.147

ers_r

2nd

52

11.11 ± 1.46

-0.081

34

11.49 ± 1.44

-0.244

0.247

-0.309

pss_pa

1st

58

44.47 ± 4.43

62

44.76 ± 4.43

0.718

-0.093

pss_pa

2nd

52

43.47 ± 4.37

0.314

34

44.27 ± 4.19

0.155

0.400

-0.251

pss_ps

1st

58

26.67 ± 8.07

62

26.61 ± 8.07

0.968

0.013

pss_ps

2nd

52

27.62 ± 7.89

-0.201

34

25.91 ± 7.26

0.148

0.303

0.362

pss

1st

58

45.21 ± 11.47

62

44.85 ± 11.47

0.867

0.052

pss

2nd

52

47.18 ± 11.21

-0.294

34

44.78 ± 10.31

0.011

0.309

0.358

rki_responsible

1st

58

20.95 ± 4.09

62

21.06 ± 4.09

0.876

-0.045

rki_responsible

2nd

52

21.02 ± 4.01

-0.029

34

21.10 ± 3.74

-0.014

0.925

-0.031

rki_nonlinear

1st

58

13.12 ± 2.84

62

13.47 ± 2.84

0.504

-0.190

rki_nonlinear

2nd

52

12.85 ± 2.79

0.148

34

13.74 ± 2.62

-0.151

0.133

-0.489

rki_peer

1st

58

20.47 ± 2.17

62

20.68 ± 2.17

0.594

-0.147

rki_peer

2nd

52

20.47 ± 2.14

-0.003

34

20.82 ± 2.02

-0.102

0.437

-0.245

rki_expect

1st

58

4.60 ± 1.03

62

4.89 ± 1.03

0.135

-0.354

rki_expect

2nd

52

4.74 ± 1.03

-0.172

34

5.11 ± 1.00

-0.283

0.096

-0.465

rki

1st

58

59.14 ± 6.04

62

60.10 ± 6.04

0.386

-0.251

rki

2nd

52

59.09 ± 5.93

0.013

34

60.81 ± 5.54

-0.187

0.172

-0.451

raq_possible

1st

58

15.74 ± 1.75

62

15.58 ± 1.75

0.616

0.125

raq_possible

2nd

52

15.36 ± 1.73

0.295

34

16.04 ± 1.67

-0.358

0.072

-0.527

raq_difficulty

1st

58

12.53 ± 1.35

62

12.31 ± 1.35

0.357

0.256

raq_difficulty

2nd

52

12.41 ± 1.33

0.144

34

12.49 ± 1.25

-0.201

0.781

-0.088

raq

1st

58

28.28 ± 2.84

62

27.89 ± 2.84

0.455

0.212

raq

2nd

52

27.80 ± 2.79

0.262

34

28.53 ± 2.62

-0.350

0.217

-0.400

who

1st

58

14.62 ± 4.46

62

14.65 ± 4.46

0.976

-0.010

who

2nd

52

14.43 ± 4.34

0.077

34

15.30 ± 3.93

-0.269

0.341

-0.357

phq

1st

58

3.66 ± 3.74

62

3.85 ± 3.74

0.770

-0.120

phq

2nd

52

3.77 ± 3.61

-0.071

34

3.83 ± 3.15

0.013

0.936

-0.036

gad

1st

58

3.38 ± 3.47

62

3.08 ± 3.47

0.638

0.161

gad

2nd

52

3.30 ± 3.38

0.045

34

3.09 ± 3.05

-0.004

0.767

0.112

nb_pcs

1st

58

51.88 ± 6.92

62

51.42 ± 6.92

0.719

0.119

nb_pcs

2nd

52

51.05 ± 6.75

0.218

34

52.11 ± 6.13

-0.180

0.452

-0.278

nb_mcs

1st

58

50.20 ± 8.42

62

50.28 ± 8.42

0.959

-0.015

nb_mcs

2nd

52

51.20 ± 8.27

-0.189

34

50.77 ± 7.71

-0.092

0.806

0.082

Between group

sets

1st

t(176.02) = 0.93, p = 0.355, Cohen d = -0.22, 95% CI (-0.40 to 1.11)

2st

t(198.50) = 1.50, p = 0.135, Cohen d = -0.43, 95% CI (-0.21 to 1.56)

setv

1st

t(163.46) = 0.69, p = 0.491, Cohen d = -0.19, 95% CI (-0.39 to 0.80)

2st

t(194.11) = 0.15, p = 0.881, Cohen d = -0.05, 95% CI (-0.63 to 0.73)

maks

1st

t(147.81) = 0.72, p = 0.470, Cohen d = -0.24, 95% CI (-0.84 to 1.82)

2st

t(182.82) = 1.02, p = 0.310, Cohen d = -0.37, 95% CI (-0.72 to 2.25)

ibs

1st

t(144.76) = 0.12, p = 0.901, Cohen d = -0.04, 95% CI (-0.80 to 0.91)

2st

t(179.40) = 1.03, p = 0.304, Cohen d = -0.39, 95% CI (-0.45 to 1.43)

ers_e

1st

t(157.28) = 0.59, p = 0.559, Cohen d = -0.17, 95% CI (-0.36 to 0.67)

2st

t(190.68) = 1.91, p = 0.057, Cohen d = -0.63, 95% CI (-0.02 to 1.15)

ers_r

1st

t(185.16) = 0.66, p = 0.513, Cohen d = -0.15, 95% CI (-0.35 to 0.71)

2st

t(200.31) = 1.16, p = 0.247, Cohen d = -0.31, 95% CI (-0.26 to 1.00)

pss_pa

1st

t(169.27) = 0.36, p = 0.718, Cohen d = -0.09, 95% CI (-1.30 to 1.89)

2st

t(196.50) = 0.84, p = 0.400, Cohen d = -0.25, 95% CI (-1.06 to 2.65)

pss_ps

1st

t(151.43) = -0.04, p = 0.968, Cohen d = 0.01, 95% CI (-2.97 to 2.85)

2st

t(186.26) = -1.03, p = 0.303, Cohen d = 0.36, 95% CI (-4.98 to 1.56)

pss

1st

t(151.31) = -0.17, p = 0.867, Cohen d = 0.05, 95% CI (-4.49 to 3.79)

2st

t(186.16) = -1.02, p = 0.309, Cohen d = 0.36, 95% CI (-7.04 to 2.24)

rki_responsible

1st

t(157.07) = 0.16, p = 0.876, Cohen d = -0.05, 95% CI (-1.36 to 1.59)

2st

t(190.54) = 0.09, p = 0.925, Cohen d = -0.03, 95% CI (-1.60 to 1.76)

rki_nonlinear

1st

t(159.14) = 0.67, p = 0.504, Cohen d = -0.19, 95% CI (-0.68 to 1.37)

2st

t(191.83) = 1.51, p = 0.133, Cohen d = -0.49, 95% CI (-0.28 to 2.06)

rki_peer

1st

t(162.21) = 0.53, p = 0.594, Cohen d = -0.15, 95% CI (-0.57 to 0.99)

2st

t(193.50) = 0.78, p = 0.437, Cohen d = -0.25, 95% CI (-0.54 to 1.25)

rki_expect

1st

t(178.70) = 1.50, p = 0.135, Cohen d = -0.35, 95% CI (-0.09 to 0.66)

2st

t(199.12) = 1.67, p = 0.096, Cohen d = -0.47, 95% CI (-0.07 to 0.81)

rki

1st

t(157.67) = 0.87, p = 0.386, Cohen d = -0.25, 95% CI (-1.22 to 3.14)

2st

t(190.93) = 1.37, p = 0.172, Cohen d = -0.45, 95% CI (-0.76 to 4.20)

raq_possible

1st

t(172.29) = -0.50, p = 0.616, Cohen d = 0.13, 95% CI (-0.79 to 0.47)

2st

t(197.48) = 1.81, p = 0.072, Cohen d = -0.53, 95% CI (-0.06 to 1.41)

raq_difficulty

1st

t(161.17) = -0.92, p = 0.357, Cohen d = 0.26, 95% CI (-0.72 to 0.26)

2st

t(192.96) = 0.28, p = 0.781, Cohen d = -0.09, 95% CI (-0.48 to 0.64)

raq

1st

t(159.51) = -0.75, p = 0.455, Cohen d = 0.21, 95% CI (-1.41 to 0.64)

2st

t(192.04) = 1.24, p = 0.217, Cohen d = -0.40, 95% CI (-0.44 to 1.90)

who

1st

t(146.10) = 0.03, p = 0.976, Cohen d = -0.01, 95% CI (-1.59 to 1.63)

2st

t(180.96) = 0.95, p = 0.341, Cohen d = -0.36, 95% CI (-0.92 to 2.65)

phq

1st

t(136.16) = 0.29, p = 0.770, Cohen d = -0.12, 95% CI (-1.15 to 1.55)

2st

t(166.55) = 0.08, p = 0.936, Cohen d = -0.04, 95% CI (-1.39 to 1.51)

gad

1st

t(145.19) = -0.47, p = 0.638, Cohen d = 0.16, 95% CI (-1.55 to 0.95)

2st

t(179.91) = -0.30, p = 0.767, Cohen d = 0.11, 95% CI (-1.59 to 1.18)

nb_pcs

1st

t(147.15) = -0.36, p = 0.719, Cohen d = 0.12, 95% CI (-2.95 to 2.04)

2st

t(182.12) = 0.75, p = 0.452, Cohen d = -0.28, 95% CI (-1.71 to 3.84)

nb_mcs

1st

t(156.55) = 0.05, p = 0.959, Cohen d = -0.02, 95% CI (-2.96 to 3.12)

2st

t(190.19) = -0.25, p = 0.806, Cohen d = 0.08, 95% CI (-3.88 to 3.02)

Within treatment group

sets

1st vs 2st

t(108.43) = 0.46, p = 0.643, Cohen d = -0.11, 95% CI (-0.54 to 0.88)

setv

1st vs 2st

t(103.12) = 0.66, p = 0.512, Cohen d = -0.15, 95% CI (-0.34 to 0.67)

maks

1st vs 2st

t(96.71) = 0.14, p = 0.889, Cohen d = -0.03, 95% CI (-0.89 to 1.02)

ibs

1st vs 2st

t(95.46) = 2.05, p = 0.043, Cohen d = -0.48, 95% CI (0.02 to 1.19)

ers_e

1st vs 2st

t(100.58) = 0.60, p = 0.550, Cohen d = -0.14, 95% CI (-0.29 to 0.54)

ers_r

1st vs 2st

t(112.62) = 1.09, p = 0.280, Cohen d = -0.24, 95% CI (-0.24 to 0.83)

pss_pa

1st vs 2st

t(105.54) = -0.68, p = 0.499, Cohen d = 0.16, 95% CI (-1.92 to 0.94)

pss_ps

1st vs 2st

t(98.19) = -0.63, p = 0.527, Cohen d = 0.15, 95% CI (-2.89 to 1.49)

pss

1st vs 2st

t(98.14) = -0.05, p = 0.964, Cohen d = 0.01, 95% CI (-3.18 to 3.04)

rki_responsible

1st vs 2st

t(100.50) = 0.06, p = 0.951, Cohen d = -0.01, 95% CI (-1.15 to 1.22)

rki_nonlinear

1st vs 2st

t(101.35) = 0.65, p = 0.516, Cohen d = -0.15, 95% CI (-0.56 to 1.12)

rki_peer

1st vs 2st

t(102.61) = 0.44, p = 0.660, Cohen d = -0.10, 95% CI (-0.51 to 0.81)

rki_expect

1st vs 2st

t(109.62) = 1.25, p = 0.213, Cohen d = -0.28, 95% CI (-0.13 to 0.59)

rki

1st vs 2st

t(100.74) = 0.80, p = 0.423, Cohen d = -0.19, 95% CI (-1.05 to 2.47)

raq_possible

1st vs 2st

t(106.82) = 1.57, p = 0.120, Cohen d = -0.36, 95% CI (-0.12 to 1.04)

raq_difficulty

1st vs 2st

t(102.18) = 0.87, p = 0.386, Cohen d = -0.20, 95% CI (-0.23 to 0.59)

raq

1st vs 2st

t(101.50) = 1.51, p = 0.134, Cohen d = -0.35, 95% CI (-0.20 to 1.49)

who

1st vs 2st

t(96.01) = 1.15, p = 0.254, Cohen d = -0.27, 95% CI (-0.48 to 1.78)

phq

1st vs 2st

t(91.88) = -0.06, p = 0.956, Cohen d = 0.01, 95% CI (-0.80 to 0.76)

gad

1st vs 2st

t(95.63) = 0.01, p = 0.988, Cohen d = -0.00, 95% CI (-0.86 to 0.87)

nb_pcs

1st vs 2st

t(96.44) = 0.77, p = 0.445, Cohen d = -0.18, 95% CI (-1.09 to 2.46)

nb_mcs

1st vs 2st

t(100.28) = 0.40, p = 0.693, Cohen d = -0.09, 95% CI (-1.94 to 2.91)

Within control group

sets

1st vs 2st

t(90.43) = -0.50, p = 0.618, Cohen d = 0.10, 95% CI (-0.76 to 0.45)

setv

1st vs 2st

t(88.85) = 1.50, p = 0.136, Cohen d = -0.29, 95% CI (-0.10 to 0.75)

maks

1st vs 2st

t(87.10) = -0.52, p = 0.603, Cohen d = 0.10, 95% CI (-1.00 to 0.59)

ibs

1st vs 2st

t(86.78) = 0.69, p = 0.495, Cohen d = -0.13, 95% CI (-0.32 to 0.65)

ers_e

1st vs 2st

t(88.14) = -1.66, p = 0.100, Cohen d = 0.32, 95% CI (-0.64 to 0.06)

ers_r

1st vs 2st

t(91.79) = 0.42, p = 0.675, Cohen d = -0.08, 95% CI (-0.36 to 0.56)

pss_pa

1st vs 2st

t(89.55) = -1.62, p = 0.108, Cohen d = 0.31, 95% CI (-2.20 to 0.22)

pss_ps

1st vs 2st

t(87.49) = 1.03, p = 0.304, Cohen d = -0.20, 95% CI (-0.88 to 2.78)

pss

1st vs 2st

t(87.48) = 1.51, p = 0.134, Cohen d = -0.29, 95% CI (-0.62 to 4.57)

rki_responsible

1st vs 2st

t(88.11) = 0.15, p = 0.884, Cohen d = -0.03, 95% CI (-0.92 to 1.07)

rki_nonlinear

1st vs 2st

t(88.35) = -0.76, p = 0.449, Cohen d = 0.15, 95% CI (-0.98 to 0.44)

rki_peer

1st vs 2st

t(88.70) = 0.02, p = 0.987, Cohen d = -0.00, 95% CI (-0.55 to 0.56)

rki_expect

1st vs 2st

t(90.80) = 0.89, p = 0.375, Cohen d = -0.17, 95% CI (-0.17 to 0.45)

rki

1st vs 2st

t(88.18) = -0.07, p = 0.946, Cohen d = 0.01, 95% CI (-1.53 to 1.43)

raq_possible

1st vs 2st

t(89.94) = -1.52, p = 0.131, Cohen d = 0.29, 95% CI (-0.87 to 0.12)

raq_difficulty

1st vs 2st

t(88.58) = -0.74, p = 0.461, Cohen d = 0.14, 95% CI (-0.47 to 0.22)

raq

1st vs 2st

t(88.39) = -1.35, p = 0.181, Cohen d = 0.26, 95% CI (-1.19 to 0.23)

who

1st vs 2st

t(86.92) = -0.40, p = 0.692, Cohen d = 0.08, 95% CI (-1.12 to 0.75)

phq

1st vs 2st

t(85.87) = 0.37, p = 0.716, Cohen d = -0.07, 95% CI (-0.53 to 0.76)

gad

1st vs 2st

t(86.82) = -0.23, p = 0.817, Cohen d = 0.05, 95% CI (-0.80 to 0.63)

nb_pcs

1st vs 2st

t(87.03) = -1.12, p = 0.267, Cohen d = 0.22, 95% CI (-2.31 to 0.65)

nb_mcs

1st vs 2st

t(88.06) = 0.97, p = 0.334, Cohen d = -0.19, 95% CI (-1.04 to 3.03)

Plot